File size: 9,202 Bytes
8a192aa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 |
# -*- coding: utf-8 -*-
## @package guided_filter.core.filters
#
# Implementation of guided filter.
# * GuidedFilter: Original guided filter.
# * FastGuidedFilter: Fast version of the guided filter.
# @author tody
# @date 2015/08/26
import numpy as np
import cv2
## Convert image into float32 type.
def to32F(img):
if img.dtype == np.float32:
return img
return (1.0 / 255.0) * np.float32(img)
## Convert image into uint8 type.
def to8U(img):
if img.dtype == np.uint8:
return img
return np.clip(np.uint8(255.0 * img), 0, 255)
## Return if the input image is gray or not.
def _isGray(I):
return len(I.shape) == 2
## Return down sampled image.
# @param scale (w/s, h/s) image will be created.
# @param shape I.shape[:2]=(h, w). numpy friendly size parameter.
def _downSample(I, scale=4, shape=None):
if shape is not None:
h, w = shape
return cv2.resize(I, (w, h), interpolation=cv2.INTER_NEAREST)
h, w = I.shape[:2]
return cv2.resize(I, (int(w / scale), int(h / scale)), interpolation=cv2.INTER_NEAREST)
## Return up sampled image.
# @param scale (w*s, h*s) image will be created.
# @param shape I.shape[:2]=(h, w). numpy friendly size parameter.
def _upSample(I, scale=2, shape=None):
if shape is not None:
h, w = shape
return cv2.resize(I, (w, h), interpolation=cv2.INTER_LINEAR)
h, w = I.shape[:2]
return cv2.resize(I, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_LINEAR)
## Fast guide filter.
class FastGuidedFilter:
## Constructor.
# @param I Input guidance image. Color or gray.
# @param radius Radius of Guided Filter.
# @param epsilon Regularization term of Guided Filter.
# @param scale Down sampled scale.
def __init__(self, I, radius=5, epsilon=0.4, scale=4):
I_32F = to32F(I)
self._I = I_32F
h, w = I.shape[:2]
I_sub = _downSample(I_32F, scale)
self._I_sub = I_sub
radius = int(radius / scale)
if _isGray(I):
self._guided_filter = GuidedFilterGray(I_sub, radius, epsilon)
else:
self._guided_filter = GuidedFilterColor(I_sub, radius, epsilon)
## Apply filter for the input image.
# @param p Input image for the filtering.
def filter(self, p):
p_32F = to32F(p)
shape_original = p.shape[:2]
p_sub = _downSample(p_32F, shape=self._I_sub.shape[:2])
if _isGray(p_sub):
return self._filterGray(p_sub, shape_original)
cs = p.shape[2]
q = np.array(p_32F)
for ci in range(cs):
q[:, :, ci] = self._filterGray(p_sub[:, :, ci], shape_original)
return to8U(q)
def _filterGray(self, p_sub, shape_original):
ab_sub = self._guided_filter._computeCoefficients(p_sub)
ab = [_upSample(abi, shape=shape_original) for abi in ab_sub]
return self._guided_filter._computeOutput(ab, self._I)
## Guide filter.
class GuidedFilter:
## Constructor.
# @param I Input guidance image. Color or gray.
# @param radius Radius of Guided Filter.
# @param epsilon Regularization term of Guided Filter.
def __init__(self, I, radius=5, epsilon=0.4):
I_32F = to32F(I)
if _isGray(I):
self._guided_filter = GuidedFilterGray(I_32F, radius, epsilon)
else:
self._guided_filter = GuidedFilterColor(I_32F, radius, epsilon)
## Apply filter for the input image.
# @param p Input image for the filtering.
def filter(self, p):
return to8U(self._guided_filter.filter(p))
## Common parts of guided filter.
#
# This class is used by guided_filter class. GuidedFilterGray and GuidedFilterColor.
# Based on guided_filter._computeCoefficients, guided_filter._computeOutput,
# GuidedFilterCommon.filter computes filtered image for color and gray.
class GuidedFilterCommon:
def __init__(self, guided_filter):
self._guided_filter = guided_filter
## Apply filter for the input image.
# @param p Input image for the filtering.
def filter(self, p):
p_32F = to32F(p)
if _isGray(p_32F):
return self._filterGray(p_32F)
cs = p.shape[2]
q = np.array(p_32F)
for ci in range(cs):
q[:, :, ci] = self._filterGray(p_32F[:, :, ci])
return q
def _filterGray(self, p):
ab = self._guided_filter._computeCoefficients(p)
return self._guided_filter._computeOutput(ab, self._guided_filter._I)
## Guided filter for gray guidance image.
class GuidedFilterGray:
# @param I Input gray guidance image.
# @param radius Radius of Guided Filter.
# @param epsilon Regularization term of Guided Filter.
def __init__(self, I, radius=5, epsilon=0.4):
self._radius = 2 * radius + 1
self._epsilon = epsilon
self._I = to32F(I)
self._initFilter()
self._filter_common = GuidedFilterCommon(self)
## Apply filter for the input image.
# @param p Input image for the filtering.
def filter(self, p):
return self._filter_common.filter(p)
def _initFilter(self):
I = self._I
r = self._radius
self._I_mean = cv2.blur(I, (r, r))
I_mean_sq = cv2.blur(I ** 2, (r, r))
self._I_var = I_mean_sq - self._I_mean ** 2
def _computeCoefficients(self, p):
r = self._radius
p_mean = cv2.blur(p, (r, r))
p_cov = p_mean - self._I_mean * p_mean
a = p_cov / (self._I_var + self._epsilon)
b = p_mean - a * self._I_mean
a_mean = cv2.blur(a, (r, r))
b_mean = cv2.blur(b, (r, r))
return a_mean, b_mean
def _computeOutput(self, ab, I):
a_mean, b_mean = ab
return a_mean * I + b_mean
## Guided filter for color guidance image.
class GuidedFilterColor:
# @param I Input color guidance image.
# @param radius Radius of Guided Filter.
# @param epsilon Regularization term of Guided Filter.
def __init__(self, I, radius=5, epsilon=0.2):
self._radius = 2 * radius + 1
self._epsilon = epsilon
self._I = to32F(I)
self._initFilter()
self._filter_common = GuidedFilterCommon(self)
## Apply filter for the input image.
# @param p Input image for the filtering.
def filter(self, p):
return self._filter_common.filter(p)
def _initFilter(self):
I = self._I
r = self._radius
eps = self._epsilon
Ir, Ig, Ib = I[:, :, 0], I[:, :, 1], I[:, :, 2]
self._Ir_mean = cv2.blur(Ir, (r, r))
self._Ig_mean = cv2.blur(Ig, (r, r))
self._Ib_mean = cv2.blur(Ib, (r, r))
Irr_var = cv2.blur(Ir ** 2, (r, r)) - self._Ir_mean ** 2 + eps
Irg_var = cv2.blur(Ir * Ig, (r, r)) - self._Ir_mean * self._Ig_mean
Irb_var = cv2.blur(Ir * Ib, (r, r)) - self._Ir_mean * self._Ib_mean
Igg_var = cv2.blur(Ig * Ig, (r, r)) - self._Ig_mean * self._Ig_mean + eps
Igb_var = cv2.blur(Ig * Ib, (r, r)) - self._Ig_mean * self._Ib_mean
Ibb_var = cv2.blur(Ib * Ib, (r, r)) - self._Ib_mean * self._Ib_mean + eps
Irr_inv = Igg_var * Ibb_var - Igb_var * Igb_var
Irg_inv = Igb_var * Irb_var - Irg_var * Ibb_var
Irb_inv = Irg_var * Igb_var - Igg_var * Irb_var
Igg_inv = Irr_var * Ibb_var - Irb_var * Irb_var
Igb_inv = Irb_var * Irg_var - Irr_var * Igb_var
Ibb_inv = Irr_var * Igg_var - Irg_var * Irg_var
I_cov = Irr_inv * Irr_var + Irg_inv * Irg_var + Irb_inv * Irb_var
Irr_inv /= I_cov
Irg_inv /= I_cov
Irb_inv /= I_cov
Igg_inv /= I_cov
Igb_inv /= I_cov
Ibb_inv /= I_cov
self._Irr_inv = Irr_inv
self._Irg_inv = Irg_inv
self._Irb_inv = Irb_inv
self._Igg_inv = Igg_inv
self._Igb_inv = Igb_inv
self._Ibb_inv = Ibb_inv
def _computeCoefficients(self, p):
r = self._radius
I = self._I
Ir, Ig, Ib = I[:, :, 0], I[:, :, 1], I[:, :, 2]
p_mean = cv2.blur(p, (r, r))
Ipr_mean = cv2.blur(Ir * p, (r, r))
Ipg_mean = cv2.blur(Ig * p, (r, r))
Ipb_mean = cv2.blur(Ib * p, (r, r))
Ipr_cov = Ipr_mean - self._Ir_mean * p_mean
Ipg_cov = Ipg_mean - self._Ig_mean * p_mean
Ipb_cov = Ipb_mean - self._Ib_mean * p_mean
ar = self._Irr_inv * Ipr_cov + self._Irg_inv * Ipg_cov + self._Irb_inv * Ipb_cov
ag = self._Irg_inv * Ipr_cov + self._Igg_inv * Ipg_cov + self._Igb_inv * Ipb_cov
ab = self._Irb_inv * Ipr_cov + self._Igb_inv * Ipg_cov + self._Ibb_inv * Ipb_cov
b = p_mean - ar * self._Ir_mean - ag * self._Ig_mean - ab * self._Ib_mean
ar_mean = cv2.blur(ar, (r, r))
ag_mean = cv2.blur(ag, (r, r))
ab_mean = cv2.blur(ab, (r, r))
b_mean = cv2.blur(b, (r, r))
return ar_mean, ag_mean, ab_mean, b_mean
def _computeOutput(self, ab, I):
ar_mean, ag_mean, ab_mean, b_mean = ab
Ir, Ig, Ib = I[:, :, 0], I[:, :, 1], I[:, :, 2]
q = (ar_mean * Ir +
ag_mean * Ig +
ab_mean * Ib +
b_mean)
return q |